DE
Zoe Dellaert
2024-10-05
Differential expression analysis of LCM RNA Data
Managing Packages Using Renv
To run this code in my project using the renv environment, run the following lines of code
install.packages("renv") #install the package on the new computer (may not be necessary if renv bootstraps itself as expected)
renv::restore() #reinstall all the package versions in the renv lockfileLoad packages
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## [88] httr_1.4.7 bit64_4.5.2 MASS_7.3-60.0.1save_ggplot <- function(plot, filename, width = 10, height = 7, units = "in", dpi = 300) {
# Display plot
print(plot)
# Save plot
ggsave(filename = paste0(filename, ".png"), plot = plot, width = width, height = height, units = units, dpi = dpi)
}
# Specify colors
ann_colors = list(
Tissue = c(OralEpi = "palegreen3" ,Aboral = "mediumpurple1")
)Read and clean count matrix and metadata
Read in raw count data
counts_raw <- read.csv("../output_RNA/stringtie-GeneExt/LCM_RNA_gene_count_matrix.csv", row.names = 1) #load in datagene_id,LCM_15,LCM_16,LCM_20,LCM_21,LCM_26,LCM_27,LCM_4,LCM_5,LCM_8,LCM_9
Read in metadata
meta <- read.csv("../data_RNA/LCM_RNA_metadata.csv") %>%
dplyr::arrange(Sample) %>%
mutate(across(c(Tissue, Fragment, Section_Date, LCM_Date), factor)) # Set variables as factors
meta$Tissue <- factor(meta$Tissue, levels = c("OralEpi","Aboral")) #we want OralEpi to be the baselineData sanity checks!
all(meta$Sample %in% colnames(counts_raw)) #are all of the sample names in the metadata column names in the gene count matrix? Should be TRUE## [1] TRUEall(meta$Sample == colnames(counts_raw)) #are they the same in the same order? Should be TRUE## [1] TRUEpOverA filtering to reduce dataset
ffun<-filterfun(pOverA(0.5,10)) #Keep genes expressed in at least 50% of samples -
counts_filt_poa <- genefilter((counts_raw), ffun) #apply filter
filtered_counts <- counts_raw[counts_filt_poa,] #keep only rows that passed filter
cat("Number of genes after filtering:", sum(counts_filt_poa))## Number of genes after filtering: 14464write.csv(filtered_counts, "../output_RNA/differential_expression/filtered_counts.csv")There are now 14464 genes in the filtered dataset.
Data sanity checks:
all(meta$Sample %in% colnames(filtered_counts)) #are all of the sample names in the metadata column names in the gene count matrix?## [1] TRUEall(meta$Sample == colnames(filtered_counts)) #are they the same in the same order? Should be TRUE## [1] TRUEDESeq2
Create DESeq object and run DESeq2
dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
colData = meta,
design= ~ Fragment + Tissue)
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing### Extract results for Aboral vs. OralEpi contrast
res <- results(dds, contrast = c("Tissue","Aboral","OralEpi"))
resLFC <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", res=res, type = "apeglm")## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895Extract results for adjusted p-value < 0.05
res <- resLFC
resOrdered <- res[order(res$pvalue),]# save differentially expressed genes
DE_05 <- as.data.frame(resOrdered) %>% filter(padj < 0.05)
DE_05_Up <- DE_05 %>% filter(log2FoldChange > 0) #Higher in Aboral, Lower in OralEpi
DE_05_Down <- DE_05 %>% filter(log2FoldChange < 0) #Lower in Aboral, Higher in OralEpi
nrow(DE_05)## [1] 3606nrow(DE_05_Up) #Higher in Aboral, Lower in OralEpi## [1] 804nrow(DE_05_Down) #Lower in Aboral, Higher in OralEpi## [1] 2802write.csv(as.data.frame(resOrdered),
file="../output_RNA/differential_expression/DESeq_results.csv")
write.csv(DE_05,
file="../output_RNA/differential_expression/DEG_05.csv")Visualizing Differential Expression
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue')
Plots
plotMA(results(dds, contrast = c("Tissue","Aboral","OralEpi")), ylim=c(-20,20))
plotMA(resLFC, ylim=c(-20,20))
Log2 Fold Change Comparison
# because we are interested in the comparison and not the intercept, we set 'coef=2'
resNorm <- lfcShrink(dds, coef=6, type="normal")## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
##
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895resAsh <- lfcShrink(dds, coef=6, type="ashr")## using 'ashr' for LFC shrinkage. If used in published research, please cite:
## Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
## https://doi.org/10.1093/biostatistics/kxw041par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-20,20)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")
plotCounts(dds, gene=which.max(res$log2FoldChange), intgroup="Tissue")
plotCounts(dds, gene=which.min(res$log2FoldChange), intgroup="Tissue")
Transforming count data for visualization
vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
ntd <- normTransform(dds) # this gives log2(n + 1)
meanSdPlot(assay(vsd), main = "vsd")## Warning in geom_hex(bins = bins, ...): Ignoring unknown parameters: `main`
meanSdPlot(assay(rld))
meanSdPlot(assay(ntd))
#save the vsd transformation
vsd_mat <- assay(vsd)
write.csv(vsd_mat, file = "../output_RNA/differential_expression/vsd_expression_matrix.csv")Will move forward with vst transformation for visualizations
Heatmap of count matrix
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
#view all genes
pheatmap(assay(vsd), cluster_rows=TRUE, show_rownames=FALSE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)),
annotation_colors = ann_colors,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))
#view highest count genes
select <- order(rowMeans(counts(dds,normalized=TRUE)),
decreasing=TRUE)[1:20]
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)),
annotation_colors = ann_colors, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))
#view most significantly differentially expressed genes
select <- order(res$padj)[1:20]
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2, annotation_col=(df%>% select(Tissue)),
annotation_colors = ann_colors,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200))
Heatmap of the sample-to-sample distances
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$Tissue, vsd$Fragment, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
Principal component plot of the samples
pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE, ntop = 14464)
percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
save_ggplot(PCA, "../output_RNA/differential_expression/PCA_allgenes")
PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
ggsave(filename = paste0("../output_RNA/differential_expression/PCA_allgenes_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
save_ggplot(PCA, "../output_RNA/differential_expression/PCA")
PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
geom_point(size=2) +
scale_color_manual(values = c("Aboral" = "mediumpurple1", "OralEpi" = "palegreen3"))+
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
ggsave(filename = paste0("../output_RNA/differential_expression/PCA_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)Clearly, the majority of the variance in the data is explained by tissue type!
Annotation data
Download annotation files from genome website
# wget files
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt.gz
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.EggNog_results.txt.gz
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.KEGG_results.txt.gz
# move to references direcotry
mv *gz ../references
# unzip files
gunzip ../references/*gzEggNog <- read.delim("../references/Pocillopora_acuta_HIv2.genes.EggNog_results.txt") %>% dplyr::rename("query" = X.query)
CDSearch <- read.delim("../references/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt", quote = "") %>% dplyr::rename("query" = X.Query)
KEGG <- read.delim("../references/Pocillopora_acuta_HIv2.genes.KEGG_results.txt", header = FALSE) %>% dplyr::rename("query" = V1, "KeggTerm" = V2)DE_05$query <- rownames(DE_05)
DE_05_annot <- DE_05 %>% left_join(CDSearch) %>% select(query,everything())## Joining with `by = join_by(query)`DE_05_eggnog <- DE_05 %>% left_join(EggNog) %>% select(query,everything())## Joining with `by = join_by(query)`write.csv(as.data.frame(DE_05_eggnog),
file="../output_RNA/differential_expression/DE_05_eggnog_annotation.csv")
annot_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(CDSearch)## Joining with `by = join_by(query)`eggnog_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(EggNog)## Joining with `by = join_by(query)`df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
gene_labels <- eggnog_all %>% select(query,PFAMs) %>%
mutate_all(~ ifelse(is.na(.), "", .)) %>% #replace NAs with "" for labelling purposes
separate(PFAMs, into = c("PFAMs", "rest of name"), sep = ",(?=.*?,)", extra = "merge")## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 12723 rows [1, 2, 3, 4,
## 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 19, 20, 21, 23, 24, 25, ...].#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi")
Genes of Interest
MarkerGenes <- read.csv("../references/Pacuta_MarkerGenes_Levy2021.csv") %>% dplyr::rename("query" = 1, "List" = 2, "definition" = 3) %>% filter(List !="Toolkit")
MarkerGenes_broc <- read.csv("../output_RNA/marker_genes/Pacuta_Spis_Markers_pairs.csv") %>% select(protein_id_spB,cluster,Standardized_Name_spA ) %>% dplyr::rename("query" = 1, "List" = 2)
MarkerGenes$def_short <- ifelse(nchar(MarkerGenes$definition) > 20,
paste0(substr(MarkerGenes$definition, 1, 17), "..."),
MarkerGenes$definition)HoxGenes_Nvec <- read.csv("../output_RNA/marker_genes/Hox_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))
HoxGenes_Nvec$def_short <- gsub("Homeobox protein", "Hox", HoxGenes_Nvec$Description)
HoxGenes_Nvec$def_short <- gsub("homeobox protein", "Hox", HoxGenes_Nvec$def_short)
He_etal_Nvec <- read.csv("../output_RNA/marker_genes/He_etal_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))
He_etal_Nvec$def_short <- gsub("Homeobox protein", "Hox", He_etal_Nvec$Description)
He_etal_Nvec$def_short <- gsub("homeobox protein", "Hox", He_etal_Nvec$def_short)
DuBuc_etal_Nvec <- read.csv("../output_RNA/marker_genes/Wnt_nematostella.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X))Biomin <- read.csv("../output_RNA/marker_genes/Pacuta_Biomin.csv") %>% dplyr::rename("query" = Pocillopora_acuta_best_hit) %>% select(-c(accessionnumber.geneID, Ref))
Biomin_broc <- read.csv("../output_RNA/marker_genes/Pacuta_Biomin_Spis_ortholog.csv") %>% dplyr::rename("query" = Pacuta_gene) %>% select(-c(X,accessionnumber_gene_id, ref))
Biomin <- Biomin %>%
group_by(query,List) %>%
summarize(definition = paste(unique(definition), collapse = ","))## `summarise()` has grouped output by 'query'. You can override using the
## `.groups` argument.Biomin$def_short <- ifelse(nchar(Biomin$definition) > 40,
paste0(substr(Biomin$definition, 1, 37), "..."),
Biomin$definition)
Biomin_filtered_counts <- filtered_counts[(rownames(filtered_counts) %in% Biomin$query),]
Biomin_broc <- Biomin_broc %>%
group_by(query,List) %>%
summarize(definition = paste(unique(definition), collapse = ","))## `summarise()` has grouped output by 'query'. You can override using the
## `.groups` argument.Biomin_broc$def_short <- ifelse(nchar(Biomin_broc$definition) > 40,
paste0(substr(Biomin_broc$definition, 1, 37), "..."),
Biomin_broc$definition)
Biomin_broc_filtered_counts <- filtered_counts[(rownames(filtered_counts) %in% Biomin_broc$query),]
write.csv(Biomin_filtered_counts, "../output_RNA/differential_expression/Biomin_filtered_counts.csv")DE_05$query <- rownames(DE_05)
resOrdered$query <- rownames(resOrdered)
DE_05_biomin <- DE_05 %>% left_join(Biomin) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DESeq_biomin <- as.data.frame(resOrdered) %>% left_join(Biomin) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_Biomin_broc <- DE_05 %>% left_join(Biomin_broc) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DESeq_Biomin_broc <- as.data.frame(resOrdered) %>% left_join(Biomin_broc) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_marker <- DE_05 %>% left_join(MarkerGenes) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_marker_broc <- DE_05 %>% left_join(MarkerGenes_broc) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_Hox <- DE_05 %>% left_join(HoxGenes_Nvec) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_He_etal <- DE_05 %>% left_join(He_etal_Nvec) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_DuBuc_etal <- DE_05 %>% left_join(DuBuc_etal_Nvec) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`write.csv(as.data.frame(DE_05_biomin),
file="../output_RNA/differential_expression/DE_05_biomin_annotation.csv")
write.csv(as.data.frame(DE_05_marker),
file="../output_RNA/differential_expression/DE_05_markergene_annotation.csv")
biomin_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin) ## Joining with `by = join_by(query)`biomin_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin) ## Joining with `by = join_by(query)`Biomin_broc_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin_broc) ## Joining with `by = join_by(query)`Biomin_broc_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin_broc) ## Joining with `by = join_by(query)`markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes) ## Joining with `by = join_by(query)`markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes) ## Joining with `by = join_by(query)`broc_markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes_broc) ## Joining with `by = join_by(query)`broc_markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes_broc) ## Joining with `by = join_by(query)`Hox_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(HoxGenes_Nvec) ## Joining with `by = join_by(query)`Hox_all_res %>% drop_na()## query baseMean log2FoldChange
## 1 Pocillopora_acuta_HIv2___TS.g15143.t1 521.8679 0.1422022
## 2 Pocillopora_acuta_HIv2___RNAseq.g12537.t1 271.6692 0.1068577
## 3 Pocillopora_acuta_HIv2___RNAseq.g24142.t1 677.5990 -0.2872155
## 4 Pocillopora_acuta_HIv2___RNAseq.g614.t1 2049.6550 3.4581160
## 5 Pocillopora_acuta_HIv2___RNAseq.18045_t 1074.8238 0.8607645
## 6 Pocillopora_acuta_HIv2___RNAseq.g3322.t1 1641.0298 -12.6389456
## 7 Pocillopora_acuta_HIv2___TS.g25076.t1 2172.4212 0.3659500
## 8 Pocillopora_acuta_HIv2___RNAseq.g1763.t1 844.6168 3.1839530
## 9 Pocillopora_acuta_HIv2___RNAseq.g9587.t1 1162.9301 1.5381041
## 10 Pocillopora_acuta_HIv2___RNAseq.g9588.t1 10129.8808 13.0019191
## 11 Pocillopora_acuta_HIv2___RNAseq.g26215.t1 665.0359 0.4339101
## 12 Pocillopora_acuta_HIv2___RNAseq.g26215.t1 665.0359 0.4339101
## lfcSE pvalue padj Gene_Name
## 1 1.0156290 1.403026e-02 5.531033e-02 B-H1
## 2 1.0129642 1.620554e-02 6.222378e-02 B-H1
## 3 0.9977356 1.453626e-01 3.413047e-01 Anthox1
## 4 1.8621899 1.683827e-04 1.284540e-03 Six4/5
## 5 1.4145958 5.029165e-03 2.338214e-02 Nkx2.2a1
## 6 3.2203985 3.690243e-11 2.629343e-09 Dlx
## 7 0.6635312 4.480396e-01 6.901301e-01 Anthox6a
## 8 1.7839837 2.174546e-04 1.603908e-03 Alx4
## 9 1.6584118 1.648080e-02 6.316330e-02 OtxC
## 10 2.9159022 2.001347e-17 1.929832e-14 OtxB
## 11 1.1064758 5.857990e-02 1.759343e-01 Anthox8a
## 12 1.1064758 5.857990e-02 1.759343e-01 Anthox8b
## Description def_short
## 1 Homeobox protein B-H1 Hox B-H1
## 2 Homeobox protein B-H1 Hox B-H1
## 3 Homeobox protein Anthox1 Hox Anthox1
## 4 Homeobox protein SIX4/5 Hox SIX4/5
## 5 Homeobox protein Nkx-2.2a Hox Nkx-2.2a
## 6 Homeobox protein Dlx Hox Dlx
## 7 Homeobox protein Anthox6a Hox Anthox6a
## 8 Homeobox protein aristaless-like 4 Hox aristaless-like 4
## 9 Homeobox protein OTX1 Hox OTX1
## 10 Homeobox protein OTX1 B Hox OTX1 B
## 11 Homeobox protein Anthox8a Hox Anthox8a
## 12 Homeobox protein Anthox8b Hox Anthox8bdf <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
#view biomin genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_biomin$query, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_biomin$def_short, fontsize_row = 5)
DE_biomin
save_ggplot(DE_biomin, "../output_RNA/differential_expression/DE_biomin")
#view biomin genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_Biomin_broc$query, ]), center = TRUE, scale = TRUE))
DE_Biomin_broc <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_Biomin_broc$def_short, fontsize_row = 5)
DE_Biomin_broc
save_ggplot(DE_Biomin_broc, "../output_RNA/differential_expression/DE_Biomin_broc")
#view marker genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_marker$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "../output_RNA/differential_expression/DE_marker")
DE_05_marker_grouped <- DE_05_marker %>% arrange(List) %>% mutate(List = as.factor(List))
z_scores <- t(scale(t(assay(vsd)[DE_05_marker_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker_grouped$List, fontsize_row = 5)
DE_05_marker_grouped_plot
save_ggplot(DE_05_marker_grouped_plot, "../output_RNA/differential_expression/DE_05_marker_grouped")
#view marker genes that are differntially expressed
z_scores <- t(scale(t(assay(vsd)[DE_05_marker_broc$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker_broc$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "../output_RNA/differential_expression/DE_marker_broc")
DE_05_marker_broc_grouped <- DE_05_marker_broc %>% arrange(List) %>% mutate(List = as.factor(List))
z_scores <- t(scale(t(assay(vsd)[DE_05_marker_broc_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_broc_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = DE_05_marker_broc_grouped$List, fontsize_row = 5)
DE_05_marker_broc_grouped_plot
save_ggplot(DE_05_marker_broc_grouped_plot, "../output_RNA/differential_expression/DE_05_marker_broc_grouped")
Visualizing Differential Expression
Biomin_volcano <- EnhancedVolcano(biomin_all_res,
lab = biomin_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 40)
save_ggplot(Biomin_volcano, "../output_RNA/differential_expression/Biomin_volcano")
Biomin_broc_volcano <- EnhancedVolcano(Biomin_broc_all_res,
lab = Biomin_broc_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 40)
save_ggplot(Biomin_broc_volcano, "../output_RNA/differential_expression/Biomin_broc_volcano")
Marker_volcano <- EnhancedVolcano(markers_all_res,
lab = markers_all_res$List,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano, "../output_RNA/differential_expression/Marker_volcano")
Marker_volcano_names <- EnhancedVolcano(markers_all_res,
lab = markers_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano_names, "../output_RNA/differential_expression/Marker_volcano_names")
Marker_volcano <- EnhancedVolcano(broc_markers_all_res,
lab = broc_markers_all_res$List,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano, "../output_RNA/differential_expression/Marker_volcano_broc")
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue')
volcano_plain <- EnhancedVolcano(resLFC,
lab = NA,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.05,
title="",
subtitle="",
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(volcano_plain, "../output_RNA/differential_expression/volcano_plain",width = 4, height = 6, units = "in", dpi = 300)
save_ggplot(volcano_plain, "../output_RNA/differential_expression/volcano_plain")
Trying to annotate un-annotated DE genes:
# wget protein sequence reference file
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz
# move to references direcotry
mv *gz ../references
# unzip files
gunzip ../references/*gz
#get the names of all the DEGs from the first column of the DEG csv file
tail -n +2 ../output_RNA/differential_expression/DEG_05.csv | cut -d',' -f1 | tr -d '"' > ../output_RNA/differential_expression/DEG_05_names.csv
#grep this file against the protein fasta file, first with wc -l to make sure the number of lines is correct (should be your number of DEGs)
grep -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa | wc -l
#grep each header with the protein sequence after ("-A 1") and save to a new file
grep -A 1 -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa > ../output_RNA/differential_expression/DEG_05_seqs.txtnr database BLAST of DE_05 genes only
On andromeda:
Blastp-ing only the DE genes against the entire nr database (will take a while)
cd ../scripts
nano DEG_05_blast.sh#!/bin/bash
#SBATCH --job-name="DE_blast"
#SBATCH -t 240:00:00
#SBATCH --export=NONE
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH --mail-user=zdellaert@uri.edu #your email to send notifications
#SBATCH --mem=500GB
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --account=putnamlab
#SBATCH --nodes=2 --ntasks-per-node=24
module load BLAST+/2.15.0-gompi-2023a
cd ../output_RNA/differential_expression #set working directory
mkdir blast
cd blast
#nr database location andromeda: /data/shared/ncbi-db/.ncbirc
# points to current location: cat /data/shared/ncbi-db/.ncbirc
# [BLAST]
# BLASTDB=/data/shared/ncbi-db/2024-11-10
blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results.txt -outfmt 0 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 10
blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results_tab.txt -outfmt 6 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1
echo "Blast complete!" $(date)sbatch DEG_05_blast.shcd ../output_RNA/differential_expression/blast
wc -l DEG_05_blast_results_tab.txt #3537 of the 3606 genes were annotated
#get just the NCBI database accession numbers for the blast results
cut -f2 DEG_05_blast_results_tab.txt > DEG_05_blast_accessions.txt
#remove any duplicates
sort -u DEG_05_blast_accessions.txt > unique_DEG_05_blast_accessions.txt
wc -l unique_DEG_05_blast_accessions.txt #3404 of the 3537 annotations were unique
while read acc; do
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=$acc&rettype=gp&retmode=text" \
| grep "DEFINITION" | sed 's/DEFINITION //g' | awk -v id="$acc" '{print id "\t" $0}'
done < unique_DEG_05_blast_accessions.txt > DEG_05_blast_names.txt
wc -l DEG_05_blast_names.txt #3396 ; unsure why 8 are missing.
join -1 2 -2 1 -t $'\t' <(sort -k2 DEG_05_blast_results_tab.txt) <(sort DEG_05_blast_names.txt) > annotated_DEG_05_blast_results_tab.txtSwissProt of ALL genes
On unity:
swissprot based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd
Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/
mkdir ../references/blast_dbscd ../references/blast_dbs
curl -O https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
mv uniprot_sprot.fasta.gz uniprot_sprot_r2024_10_02.fasta.gz
gunzip -k uniprot_sprot_r2024_10_02.fasta.gz
rm uniprot_sprot_r2024_10_02.fasta.gz
head uniprot_sprot_r2024_10_02.fasta
echo "Number of Sequences"
grep -c ">" uniprot_sprot_r2024_10_02.fasta
# 572214 sequencesmodule load blast-plus/2.14.1
makeblastdb \
-in ../references/blast_dbs/uniprot_sprot_r2024_10_02.fasta \
-dbtype prot \
-out ../references/blast_dbs/uniprot_sprot_r2024_10_02cd ../scripts
nano blastp_SwissProt.sh#!/bin/bash
#SBATCH -t 18:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=48
#SBATCH --mem=500GB
#SBATCH --export=NONE
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH -D /project/pi_hputnam_uri_edu/zdellaert/LaserCoral #set working directory
module load blast-plus/2.14.1
cd references/
mkdir annotation
fasta="Pocillopora_acuta_HIv2.genes.pep.faa"
blastp \
-query $fasta \
-db blast_dbs/uniprot_sprot_r2024_10_02 \
-out annotation/blastp_SwissProt_out.tab \
-evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1 \
-outfmt 6
echo "Blast complete!" $(date)cd references/annotation/
tr '|' '\t' < blastp_SwissProt_out.tab > blastp_SwissProt_out_sep.tabcd ../references/annotation/
curl -H "Accept: text/plain; format=tsv" "https://rest.uniprot.org/uniprotkb/stream?fields=accession%2Creviewed%2Cid%2Cprotein_name%2Cgene_names%2Corganism_name%2Clength%2Cgo_p%2Cgo%2Cgo_id%2Cgo_c%2Cgo_f&format=tsv&query=%28reviewed%3Atrue%29" -o SwissProt-Annot-GO_111524.tsv
wc -l SwissProt-Annot-GO_111524.tsv
#572215All code below based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd
Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/
bltabl <- read.csv("../references/annotation/blastp_SwissProt_out_sep.tab", sep = '\t', header = FALSE)
spgo <- read.csv("../references/annotation/SwissProt-Annot-GO_111524.tsv", sep = '\t', header = TRUE)annot_tab <- left_join(bltabl, spgo, by = c("V3" = "Entry")) %>%
select(
query = V1,
blast_hit = V3,
evalue = V13,
ProteinNames = Protein.names,
BiologicalProcess = Gene.Ontology..biological.process.,
GeneOntologyIDs = Gene.Ontology.IDs
)
head(annot_tab)## query blast_hit evalue
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a Q4JAI4 1.02e-37
## 2 Pocillopora_acuta_HIv2___RNAseq.g18333.t1 O08807 9.62e-116
## 3 Pocillopora_acuta_HIv2___RNAseq.g7985.t1 O74212 3.56e-158
## 4 Pocillopora_acuta_HIv2___TS.g15308.t1 Q09575 1.08e-12
## 5 Pocillopora_acuta_HIv2___RNAseq.g2057.t1 P0C1P0 8.81e-14
## 6 Pocillopora_acuta_HIv2___RNAseq.g4696.t1 Q9W2Q5 8.98e-69
## ProteinNames
## 1 Methionine synthase (EC 2.1.1.-) (Homocysteine methyltransferase)
## 2 Peroxiredoxin-4 (EC 1.11.1.24) (Antioxidant enzyme AOE372) (Peroxiredoxin IV) (Prx-IV) (Thioredoxin peroxidase AO372) (Thioredoxin-dependent peroxide reductase A0372) (Thioredoxin-dependent peroxiredoxin 4)
## 3 Acyl-lipid (8-3)-desaturase (EC 1.14.19.30) (Delta(5) fatty acid desaturase) (Delta-5 fatty acid desaturase)
## 4 Uncharacterized protein K02A2.6
## 5 Phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (Phosphatidylinositol-glycan biosynthesis class Y protein) (PIG-Y)
## 6 Calcium and integrin-binding family member 2
## BiologicalProcess
## 1 methionine biosynthetic process [GO:0009086]; methylation [GO:0032259]
## 2 cell redox homeostasis [GO:0045454]; extracellular matrix organization [GO:0030198]; hydrogen peroxide catabolic process [GO:0042744]; male gonad development [GO:0008584]; negative regulation of male germ cell proliferation [GO:2000255]; protein maturation by protein folding [GO:0022417]; reactive oxygen species metabolic process [GO:0072593]; response to oxidative stress [GO:0006979]; spermatogenesis [GO:0007283]
## 3 long-chain fatty acid biosynthetic process [GO:0042759]; unsaturated fatty acid biosynthetic process [GO:0006636]
## 4 DNA integration [GO:0015074]
## 5 GPI anchor biosynthetic process [GO:0006506]
## 6 calcium ion homeostasis [GO:0055074]; phototransduction [GO:0007602]
## GeneOntologyIDs
## 1 GO:0003871; GO:0008270; GO:0009086; GO:0032259
## 2 GO:0005615; GO:0005737; GO:0005739; GO:0005783; GO:0005790; GO:0005829; GO:0006979; GO:0007283; GO:0008379; GO:0008584; GO:0022417; GO:0030198; GO:0042744; GO:0042802; GO:0045454; GO:0072593; GO:0140313; GO:2000255
## 3 GO:0006636; GO:0016020; GO:0020037; GO:0042759; GO:0046872; GO:0102866
## 4 GO:0003676; GO:0005737; GO:0008270; GO:0015074; GO:0019899; GO:0042575
## 5 GO:0000506; GO:0006506
## 6 GO:0000287; GO:0005509; GO:0005737; GO:0007602; GO:0055074write.table(annot_tab,
file = "../references/annotation/protein-GO.tsv",
sep = "\t",
row.names = FALSE,
quote = FALSE)
DE_05_SwissProt <- DE_05 %>% left_join(annot_tab) %>% select(query,everything()) ## Joining with `by = join_by(query)`write.csv(as.data.frame(DE_05_SwissProt),
file="../output_RNA/differential_expression/DE_05_SwissProt_annotation.csv")df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 30,
paste0(substr(DE_05_SwissProt$ProteinNames, 1, 27), "..."),
DE_05_SwissProt$ProteinNames)
gene_labels <- DE_05_SwissProt %>%
select(query,short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
rownames(res)[select]## [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"
## [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1"
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1"
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1"
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1"
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1"
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_SwissProt")
#view most significantly differentially expressed genes by LFC
select <- order(abs(res$log2FoldChange),decreasing = TRUE)[1:50]
rownames(res)[select]## [1] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [2] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [5] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [8] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g22261.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [18] "Pocillopora_acuta_HIv2___TS.g4983.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [20] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [21] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [24] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [30] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [31] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [33] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g9631.t1"
## [37] "Pocillopora_acuta_HIv2___RNAseq.g7627.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g8062.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g1102.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g14484.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g21373.t1"
## [42] "Pocillopora_acuta_HIv2___TS.g16384.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g13561.t1"
## [44] "Pocillopora_acuta_HIv2___RNAseq.g1126.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g9210.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.g24649.t1"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g11659.t1"
## [48] "Pocillopora_acuta_HIv2___TS.g25577.t1a"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g27681.t1b"
## [50] "Pocillopora_acuta_HIv2___TS.g1968.t2"z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_LFC_DE_SwissProt")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_SwissProt")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_SwissProt")
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
DE_05_SwissProt$short_GO <- ifelse(nchar(DE_05_SwissProt$BiologicalProcess) > 30,
paste0(substr(DE_05_SwissProt$BiologicalProcess, 1, 27), "..."),
DE_05_SwissProt$BiologicalProcess)
gene_labels <- DE_05_SwissProt %>% select(query,short_GO) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_Blast2GO")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_Blast2GO")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_Blast2GO")
Combined annotations manually
Manual <- read.csv("../output_RNA/differential_expression/DE_05_Manual_annotation.csv") %>% dplyr::rename("query" = 2, "definition" = 3) %>% arrange(X) df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
rownames(res)[select]## [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"
## [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1"
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1"
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1"
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1"
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1"
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_Manual")
#view most significantly differentially expressed genes by LFC
select <- order(abs(res$log2FoldChange),decreasing = TRUE)[1:50]
rownames(res)[select]## [1] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
## [2] "Pocillopora_acuta_HIv2___TS.g18104.t1"
## [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1"
## [4] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1"
## [5] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"
## [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3"
## [7] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1"
## [8] "Pocillopora_acuta_HIv2___TS.g30765.t1"
## [9] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"
## [10] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1"
## [11] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1"
## [12] "Pocillopora_acuta_HIv2___RNAseq.11056_t"
## [13] "Pocillopora_acuta_HIv2___RNAseq.g22261.t1"
## [14] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1"
## [15] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1"
## [16] "Pocillopora_acuta_HIv2___TS.g19991.t2"
## [17] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1"
## [18] "Pocillopora_acuta_HIv2___TS.g4983.t1"
## [19] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1"
## [20] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1"
## [21] "Pocillopora_acuta_HIv2___TS.g9414.t1"
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"
## [23] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1"
## [24] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1"
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"
## [26] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"
## [27] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"
## [28] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1"
## [29] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [30] "Pocillopora_acuta_HIv2___RNAseq.30415_t"
## [31] "Pocillopora_acuta_HIv2___TS.g16008.t2"
## [32] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"
## [33] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1"
## [34] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1"
## [36] "Pocillopora_acuta_HIv2___RNAseq.g9631.t1"
## [37] "Pocillopora_acuta_HIv2___RNAseq.g7627.t1"
## [38] "Pocillopora_acuta_HIv2___RNAseq.g8062.t1"
## [39] "Pocillopora_acuta_HIv2___RNAseq.g1102.t1"
## [40] "Pocillopora_acuta_HIv2___RNAseq.g14484.t1"
## [41] "Pocillopora_acuta_HIv2___RNAseq.g21373.t1"
## [42] "Pocillopora_acuta_HIv2___TS.g16384.t1"
## [43] "Pocillopora_acuta_HIv2___RNAseq.g13561.t1"
## [44] "Pocillopora_acuta_HIv2___RNAseq.g1126.t1"
## [45] "Pocillopora_acuta_HIv2___RNAseq.g9210.t1"
## [46] "Pocillopora_acuta_HIv2___RNAseq.g24649.t1"
## [47] "Pocillopora_acuta_HIv2___RNAseq.g11659.t1"
## [48] "Pocillopora_acuta_HIv2___TS.g25577.t1a"
## [49] "Pocillopora_acuta_HIv2___RNAseq.g27681.t1b"
## [50] "Pocillopora_acuta_HIv2___TS.g1968.t2"z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_LFC_DE_Manual")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), labels_col = NA, annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 8.5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_Manual")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), labels_col = NA, annotation_colors = ann_colors,
labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 8.5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_Manual")
Expression of certain genes of interest: blast against P. acuta genome
yin yang Transctiption factor
cd ../references
#download the genome protein fasta if you have not already
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz
#unzip file
gunzip Pocillopora_acuta_HIv2.genes.pep.faa.gzIn unity
salloc -p cpu -c 8 --mem 32G
module load uri/main
module load BLAST+/2.15.0-gompi-2023a
#make blast_dbs directory if you haven't done so above
mkdir blast_dbs
cd blast_dbs
makeblastdb -in ../Pocillopora_acuta_HIv2.genes.pep.faa -out Pacuta_prot -dbtype prot
cd ../../output_RNA/differential_expression/
#make blast output directory if you haven't done so above
mkdir blast
cd blast
nano YinYang.txtadd accession numbers of interest:
- nematostella vectensis transcriptional repressor protein YY1
isoforms
- XP_048585772.1
- XP_048585773.1
- XP_048585774.1
- human transcription factor YY2
- NP_996806.2
- human transcription factor ZFP41 (REX-1)
- NP_777560.2
XP_048585772.1
XP_048585773.1
XP_048585774.1
NP_996806.2
NP_777560.2# Read the input file line by line and fetch FASTA sequences
while read -r accession; do
if [[ -n "$accession" ]]; then
echo "Fetching $accession..."
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=${accession}&rettype=fasta&retmode=text" >> "YinYang.fasta"
echo >> "YinYang.fasta" # Add a newline between sequences
sleep 1 # Avoid hitting rate limits
fi
done < "YinYang.txt"
# run blast with human-readable output
blastp -query YinYang.fasta -db ../../../references/blast_dbs/Pacuta_prot -out YinYang_blast_results.txt -outfmt 0
#looks like there are a lot of matches for each gene! I am going to do a tab search with a very low e-value cutoff:
# run blast with tabular output
blastp -query YinYang.fasta -db ../../../references/blast_dbs/Pacuta_prot -out YinYang_blast_results_tab.txt -outfmt 6 -evalue 1e-25Great! Interestingly, the same gene, Pocillopora_acuta_HIv2_RNAseq.g25242.t1 is the top match for all 5 proteins I searched. Pocillopora_acuta_HIv2_TS.g24434.t1 is the second best match for all 5. That is interesting! Pocillopora_acuta_HIv2_RNAseq.g7583.t1 is also worth looking at. And Pocillopora_acuta_HIv2_TS.g21338.t1 matched all three YY1 isoforms.
YinYangs <- c("Pocillopora_acuta_HIv2___RNAseq.g25242.t1",
"Pocillopora_acuta_HIv2___TS.g24434.t1",
"Pocillopora_acuta_HIv2___RNAseq.g7583.t1",
"Pocillopora_acuta_HIv2___TS.g21338.t1")
for (i in 1:length(YinYangs)){
plotCounts(dds, gene=YinYangs[i], intgroup=c("Tissue"),)
}
as.data.frame(resOrdered)[YinYangs,]## baseMean log2FoldChange lfcSE
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 2388.43282 -0.65267532 0.8125595
## Pocillopora_acuta_HIv2___TS.g24434.t1 86.14232 -0.16148954 1.0080043
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1 429.08727 0.06121494 0.9629085
## Pocillopora_acuta_HIv2___TS.g21338.t1 277.12637 -0.03463576 1.0095974
## pvalue padj
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 0.1695296 0.3778825
## Pocillopora_acuta_HIv2___TS.g24434.t1 0.2933826 0.5402220
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1 0.8212358 0.9266210
## Pocillopora_acuta_HIv2___TS.g21338.t1 0.2453329 0.4838418
## query
## Pocillopora_acuta_HIv2___RNAseq.g25242.t1 Pocillopora_acuta_HIv2___RNAseq.g25242.t1
## Pocillopora_acuta_HIv2___TS.g24434.t1 Pocillopora_acuta_HIv2___TS.g24434.t1
## Pocillopora_acuta_HIv2___RNAseq.g7583.t1 Pocillopora_acuta_HIv2___RNAseq.g7583.t1
## Pocillopora_acuta_HIv2___TS.g21338.t1 Pocillopora_acuta_HIv2___TS.g21338.t1Okay! None of these genes are differentially expressed between the tissues. That is interesting and good to know. Pocillopora_acuta_HIv2___RNAseq.g25242.t1 has the highest basal expression of all the potential isoforms of this transcription factor.
TRP Channels
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 80,
paste0(substr(DE_05_SwissProt$ProteinNames, 1, 77), "..."),
DE_05_SwissProt$ProteinNames)
gene_labels <- DE_05_SwissProt %>%
select(query,short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
TRP <- Manual %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select <- TRP$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 12)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/TRP_DE_SwissProt", width = 6.43, height = 4.25, units = "in", dpi = 300)
annot_tab$short_name <- ifelse(nchar(annot_tab$ProteinNames) > 80,
paste0(substr(annot_tab$ProteinNames, 1, 77), "..."),
annot_tab$ProteinNames)
gene_labels <- annot_tab %>%
select(query,short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
TRP <- annot_tab %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select1 <- TRP$query
select <- match(select1,rownames(vsd))
select <- select[!is.na(select)]
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select1,gene_labels$query),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/TRP_SwissProt")
biomin de heatmap nice
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
DE_05_biomin_filtered <- DE_05_biomin %>% left_join(Manual,by="query" ) %>% filter(!(Heatmap_Label %in% c("Myosin-9", "Actin, cytoplasmic")))
select <- DE_05_biomin_filtered$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
DE_biomin
save_ggplot(DE_biomin, "../output_RNA/differential_expression/DE_biomin")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k as needed
# Create a dataframe to manage clusters and reordering
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate heatmap with reordered rows and labels
DE_biomin <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
show_rownames = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8
)
save_ggplot(DE_biomin, "../output_RNA/differential_expression/clusters_clean/DE_biomin")
#############################
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
DE_05_Biomin_broc_filtered <- DE_05_Biomin_broc %>% left_join(Manual,by="query" ) %>% filter(!(Heatmap_Label %in% c("Myosin-9", "Actin, cytoplasmic")))
select <- DE_05_Biomin_broc_filtered$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
DE_Biomin_broc <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
DE_Biomin_broc
save_ggplot(DE_Biomin_broc, "../output_RNA/differential_expression/DE_Biomin_broc")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k as needed
# Create a dataframe to manage clusters and reordering
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate heatmap with reordered rows and labels
DE_Biomin_broc <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
show_rownames = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8
)
save_ggplot(DE_Biomin_broc, "../output_RNA/differential_expression/clusters_clean/DE_Biomin_broc")
###wnt and hox
WNT <- Manual %>% filter(grepl("wnt", Heatmap_Label, ignore.case = TRUE)|grepl("frizzle", Heatmap_Label, ignore.case = TRUE)|grepl("homeobox", Heatmap_Label, ignore.case = TRUE)|grepl("hox", Heatmap_Label, ignore.case = TRUE)|grepl("forkhead", Heatmap_Label, ignore.case = TRUE))#|grepl("wnt", BiologicalProcess, ignore.case = TRUE))
select <- WNT$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/WNT_Hox_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 24 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/WNT_Hox_DE_SwissProt")
Hox genes Nematostella
differentially expressed
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
select <- DE_05_Hox$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/Hox_Nvec_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 4 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/Hox_Nvec_DE_SwissProt")
all
labels <- Hox_all_res %>% drop_na() %>% pull(def_short)
select <- Hox_all_res %>% drop_na() %>% pull(query)
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = labels, fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/Hox_Nvec_all_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = labels,
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 9 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/Hox_Nvec_all_SwissProt")
He et al 2023 TFs/genes of interest Nematostella
differentially expressed
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
select <- unique(DE_05_He_etal$query)
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/He_etal_Nvec_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = 4 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/He_etal_Nvec_DE_SwissProt")
### DuBuc et al 2018 Wnt/Aboral-Oral Patterning Genes, interest
Nematostella
differentially expressed
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
select <- unique(DE_05_DuBuc_etal$query)
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/DuBuc_etal_Nvec_DE_SwissProt")
# Perform clustering
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Ensure there is only 1 gap between clusters
#row_breaks <- which(cluster_assignments != cluster_assignments[1]) # Only where cluster switches
# Generate the heatmap with reordered rows and labels
top50_DE <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white"#, # Optional border around clusters
#cutree_rows = 2, # Keep row clustering if desired
#gaps_row = 4 # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(top50_DE, "../output_RNA/differential_expression/clusters_clean/DuBuc_etal_Nvec_DE_SwissProt")
bacteria genes
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
mucin <- Manual %>% filter(grepl("mucin", ProteinNames, ignore.case = TRUE)|grepl("toll-", ProteinNames, ignore.case = TRUE)|grepl("ZP", ProteinNames, ignore.case = TRUE)|grepl("lectin", ProteinNames, ignore.case = TRUE)|grepl("nitric", Heatmap_Label, ignore.case = TRUE)) %>% filter(Heatmap_Label !="Cnidocyte marker protein (Collectin-11)")
select <- mucin$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/bacteria_DE_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate the heatmap with reordered rows and labels
mucins <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = c(24,38) # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(mucins, "../output_RNA/differential_expression/clusters_clean/bacteria_DE_SwissProt")
Symbiont related transporters
transport <- Manual %>% filter(grepl("amino acid transport", BiologicalProcess, ignore.case = TRUE)|grepl("ammonium transmembrane transport", BiologicalProcess, ignore.case = TRUE))
select <- transport$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 2)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/transport_DE_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 2) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate the heatmap with reordered rows and labels
transporters_heat <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = c(16) # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(transporters_heat, "../output_RNA/differential_expression/clusters_clean/transport_DE_SwissProt")
temp and light sensing
Manual <- read.csv("../output_RNA/differential_expression/DE_05_Manual_annotation.csv") %>% dplyr::rename("query" = 2, "definition" = 3) %>% arrange(X)
gene_labels <- Manual %>%
select(query,Heatmap_Label) %>%
mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes
sensors <- Manual %>% filter(grepl("TRP", Heatmap_Label, ignore.case = TRUE)|grepl("cellular response to light stimulus", BiologicalProcess, ignore.case = TRUE)|grepl("detection of mechanical stimulus involved in sensory perception", BiologicalProcess, ignore.case = TRUE))
select <- sensors$query
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=(df%>% select(Tissue)), annotation_colors = ann_colors,
labels_row = gene_labels[match(select,(gene_labels$query)),2], fontsize_row = 8, cutree_rows = 3)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/sensors_DE_SwissProt")
# Perform clustering
row_clusters <- hclust(dist(z_scores))
cluster_assignments <- cutree(row_clusters, k = 3) # Adjust k for the number of clusters
# Create a dataframe for clustering and label management
clustered_data <- data.frame(
query = select,
Heatmap_Label = gene_labels[match(select, gene_labels$query), 2],
cluster = cluster_assignments
)
# Reorder rows within each cluster alphabetically by their labels
clustered_data <- clustered_data %>%
mutate(cluster = factor(cluster, levels = c(1, 3, 2))) %>%
arrange(cluster, Heatmap_Label)
# Reorder the z_scores matrix and labels based on the new order
z_scores <- z_scores[match(clustered_data$query, rownames(z_scores)), ]
ordered_labels <- clustered_data$Heatmap_Label
# Generate the heatmap with reordered rows and labels
transporters_heat <- pheatmap(
z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = FALSE, # Disable clustering since rows are pre-ordered
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = (df %>% select(Tissue)),
annotation_colors = ann_colors,
labels_row = ordered_labels,
fontsize_row = 8,
borders_color = "white", # Optional border around clusters
cutree_rows = 2, # Keep row clustering if desired
gaps_row = c(10,18) # Add breaks only between clusters
)
# Save the heatmap
save_ggplot(transporters_heat, "../output_RNA/differential_expression/clusters_clean/sensors_DE_SwissProt")
random
# Create annotation data frame
df <- as.data.frame(colData(dds)[, c("Tissue", "Fragment")])
# Function to generate short names for proteins
generate_short_name <- function(data) {
data %>%
mutate(short_name = ifelse(nchar(ProteinNames) > 60,
paste0(substr(ProteinNames, 1, 57), "..."),
ProteinNames))
}
# Function to create gene labels
create_gene_labels <- function(data) {
data %>%
select(query, short_name) %>%
mutate_all(~ ifelse(is.na(.), "", .)) # Replace NAs with ""
}
# Function to generate z-scores for selected genes
calculate_z_scores <- function(data, selection, vsd_matrix) {
selected_genes <- match(selection, rownames(vsd_matrix))
selected_genes <- selected_genes[!is.na(selected_genes)] # Remove NAs
t(scale(t(assay(vsd_matrix)[selected_genes, ]), center = TRUE, scale = TRUE))
}
# Function to create and save heatmap
create_heatmap <- function(z_scores, labels_row, annotation_col, annotation_colors, output_path) {
heatmap <- pheatmap(z_scores,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = TRUE,
show_rownames = TRUE,
cluster_cols = TRUE,
cutree_cols = 2,
annotation_col = annotation_col,
annotation_colors = annotation_colors,
labels_row = labels_row,
fontsize_row = 6)
save_ggplot(heatmap, output_path)
}
# Process DE_05_SwissProt
DE_05_SwissProt <- generate_short_name(DE_05_SwissProt)
gene_labels <- create_gene_labels(DE_05_SwissProt)
TRP <- DE_05_SwissProt %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
select <- TRP$query
z_scores <- calculate_z_scores(DE_05_SwissProt, select, vsd)
create_heatmap(z_scores,
labels_row = gene_labels[match(select, gene_labels$query), 2],
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/TRP_DE_SwissProt")
# Process annot_tab
annot_tab <- generate_short_name(annot_tab)
gene_labels <- create_gene_labels(annot_tab)
TRP <- annot_tab %>% filter(grepl("transient", ProteinNames, ignore.case = TRUE))
TRP <- left_join(TRP, as.data.frame(resOrdered)) %>% filter(!is.na(log2FoldChange))## Joining with `by = join_by(query)`select1 <- TRP$query
z_scores <- calculate_z_scores(annot_tab, select1, vsd)
create_heatmap(z_scores,
labels_row = TRP$short_name,
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/TRP_SwissProt")
significant <- ifelse(row.names(z_scores) %in% DE_05$query, "Significant", "Not Significant")
# Add this as a new annotation
row_annotation <- data.frame(Significance = significant)
rownames(row_annotation) <- rownames(z_scores)
row_annotation_colors <- list(Significance = c("Significant" = "red", "Not Significant" = "grey"))
heatmap <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),
cluster_rows = TRUE,
show_rownames = TRUE,
cluster_cols = TRUE,
cutree_cols = 2,
cutree_rows = 5,
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
annotation_row = row_annotation,
annotation_row_colors = row_annotation_colors,
labels_row = TRP$short_name,
fontsize_row = 12)
save_ggplot(heatmap, "../output_RNA/differential_expression/TRP_SwissProt")
# Process DE_05_SwissProt
DE_05_SwissProt <- generate_short_name(DE_05_SwissProt)
gene_labels <- create_gene_labels(DE_05_SwissProt)
GFP <- DE_05_SwissProt %>% filter(grepl("biolum", BiologicalProcess, ignore.case = TRUE))
select <- GFP$query
z_scores <- calculate_z_scores(DE_05_SwissProt, select, vsd)
create_heatmap(z_scores,
labels_row = gene_labels[match(select, gene_labels$query), 2],
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/GFP_DE_SwissProt")
# Process annot_tab
annot_tab <- generate_short_name(annot_tab)
gene_labels <- create_gene_labels(annot_tab)
GFP <- annot_tab %>% filter(grepl("biolum", BiologicalProcess, ignore.case = TRUE))
select1 <- GFP$query
z_scores <- calculate_z_scores(annot_tab, select1, vsd)
create_heatmap(z_scores,
labels_row = gene_labels[match(select1, gene_labels$query), 2],
annotation_col = df %>% select(Tissue),
annotation_colors = ann_colors,
output_path = "../output_RNA/differential_expression/GFP_SwissProt")
Aboral Oral Larvae Paper - Pdam genome
Join our data to Pdam annotations
Annot_Pdam <- read.csv("../references/annotation/blastp_Pdam_out.tab", sep = '\t', header = FALSE) %>% select(c(1,2)) %>% dplyr::rename("protein_id" = "V2", "query" = "V1")Manual_Pdam <- left_join(Manual, Annot_Pdam) ## Joining with `by = join_by(query)`library(readxl)
larval_aboral_enriched <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval.xlsx", sheet = "pocillopora_aboral_enriched_05_")
Manual_Pdam_upaboral <- Manual_Pdam %>% filter(log2FoldChange > 0)
inner_join(larval_aboral_enriched, Manual_Pdam_upaboral, by=c("gene ID"="protein_id")) %>% dim()## [1] 18 26#18 genes overlap from the 126 in the paper
larval_oral_enriched <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval.xlsx", sheet = "pocillopora_oral_enriched_05_lf")
Manual_Pdam_uporal <- Manual_Pdam %>% filter(log2FoldChange < 0)
inner_join(larval_oral_enriched, Manual_Pdam_uporal, by=c("gene ID"="protein_id")) %>% dim()## [1] 6 26inner_join(larval_oral_enriched, Manual_Pdam_uporal, by=c("gene ID"="protein_id")) %>% View()
#only 6/83 genes overlap
larval_aboral_clusters <- read_excel("../output_RNA/marker_genes/RamonMateu_Pdam_larval_scRNA.xlsx") %>% filter(!is.na(pocillopora))## New names:
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`larval_aboral_clusters_Pacuta <- inner_join(larval_aboral_clusters, Manual_Pdam, by=c("pocillopora"="protein_id"))
#my genes upregulated in oral tissue are high in mucous cells, coolUpdating Renv environment:
After you’ve confirmed your code works as expected, use renv::snapshot() to record the packages and their sources in the lockfile.
renv::snapshot()